CO 2 emissions: trends and future pathways (Houston, we have a problem)

Transcription

1 CO 2 emissions: trends and future pathways (Houston, we have a problem) Michael Raupach ESSP Global Carbon Project Centre for Australian Weather and Climate Research with Pep Canadell, Cathy Trudinger, Peter Rayner Thanks: GCP colleagues, CSIRO colleagues Workshop on Urban Energy and Carbon Modelling, Bangkok, 4-6 February 28

2 Scope Outline Climate change and the global C cycle Drivers: Kaya and extended Kaya identities Emissions trends and targets Main message This is not a standard pollution problem Options within reach (including urban) will only fix some of it After that, transformations are needed: many of these will be urban

3 Components of radiative forcing CO 2 other gases nongas IPCC (27) WG1

4 Global temperature predictions for 19 to 21 (IPCC Fourth Assessment, Feb 27) Future CO 2 emissions (2-21) High A2 17 GtC Medium A1B 15 GtC Low B1 92 GtC None ~25 GtC Actual CO 2 emissions 2 deg Climate system inertia IPCC (27) Fourth Assessment, WG1 SPM, Fig 7

5 Canadell, Le Quere, Raupach et al. (27) PNAS Budget of atmospheric CO 2 Source from land use change Source from fossil-fuel emissions Ocean sink Atmospheric accumulation = F Foss + F LUC + F LandAir + F OceanAir : 45% of total emissions remain in atmosphere Land sink

6 Global CO 2 emissions from fossil fuels Growth rates: : 1.3 % y : 3.3 % y 1 Scenarios underestimate actual emissions since 2 CO 2 Emissions (GtC y -1 ) Actual emissions: CDIAC 45ppm stabilisation 65ppm stabilisation A1FI A1B A1T A2 B1 B2 Recent emissions Raupach et al. (27) PNAS Fossil Fuel Emission (GtC/y) CDIAC EIA scaled IEAall scaled A1B(Av) A1FI(Av) A1T(Av) A2(Av) B1(Av) B2(Av) Stabilisation 45 Stabilisation 65 Revised for 26 Everything scaled to CDIAC 2 CO 2 Emissions (GtC y -1 ) Actual emissions: CDIAC Actual emissions: EIA 45ppm stabilisation 65ppm stabilisation A1FI A1B A1T A2 B1 B

7 Raupach et al. (27) PNAS Raupach et al. (28) in prep Kaya and extended Kaya identities Kaya identity F E = P g h E CO 2 emissions (fossil + LUC) = Pop Per capita GDP Carbon intensity of GDP (fossil + LUC emissions) Extended Kaya identity dc a /dt = a E P g h E CO 2 accumulation = Airborne fraction Pop Per capita GDP Carbon intensity of GDP (fossil + LUC emissions) Notes: F E includes Foss and LUC g = (GDP-PPP) / Pop h E = (Foss + LUC) / (GDP=PPP)

8 Drivers of emissions and CO 2 growth Kaya identity F E = P g h E growth rates (%/y): 1.7 = Factor Extended Kaya identity dc a /dt = a E P g h E growth rates (%/y): 1.9 = Factor Raupach et al. (27) PNAS Raupach et al. (28) in prep

9 Energy, emissions and wealth (24) 1 1 Per capita primary energy, E/P [kw/person] Per-capita energy use as function (per-capita income) Per-capita emissions as a function (per-capita income) Income measure: GDP-PPP regions 3 D1 nations World KyotoA1 Slope Per capita emissions, F/P [tc/y/person] 1 Income (g=gp/p) 1 Energy use and fossil fuel emissions both increase linearly with wealth over a factor of > regions 3 D1 nations World KyotoA1 Slope 1 Raupach et al. (28) in prep Income (g=gp/p)

10 Raupach et al. (28) in prep Development trajectories for emissions Plot per capita FF emissions against income, in 198, 1992, 24 Per capita FF emissions (tc/y/person) F/P Per capita emissions, F/P [tc/y/person] China FSU World India Australia KyA1 Canada Taiwan EU Japan 25 USA 25 USA EU Japan D1 FSU China India D2 D3 World Australia Canada Taiw an KyotoA Income (g=gp/p) Per capita income (k$/y/person, Y2 US dollars, GDP-PPP)

11 Raupach et al. (28) in prep Sharing future emissions Cumulative emissions (Q) can be shared in 2 basic ways: by present emissions (F i ) => nation i gets Q i = QF i /F by population (P i ) => nation i gets Q i = QP i /P Sharing algorithm must lie between these limits, so quota (Q i ) for country i is: Fi Pi Qi = Q ( 1 w) + w F P ( with F = Fi, P = Pi) Time to exhaust quota at current emission: = ( 1 ) + F P Ti T w w F i P i Fi with Ti =, T = Qi F Q Weight w is a "differentiation index" between and 1

12 Raupach et al. (28) in prep Sharing future emissions Time scale = F i /Q i = time to exhaust quotas (Q i ) at constant 24 emission rates assume global cap on fossil-fuel emissions Q = 5 GtC Time (y) to exhaust quota with emissions frozen at 2 levels USA w=: w=weight by emissions (wealth) w=.5: w=.5 compromise w=1: w=1 weight by population (equity) USA 62, 38, 13 EU Japan D1 India 62, 161, 262 FSU China India D2 D3 World World 62, 62, 62 Australia D3 62, 67, 13 Canada Taiwan KyotoA1 Easier Harder

13 UN Development Program data (August 26) Global trends in urbanisation World 15 More Developed Population (million) Urban > 1M Urban 5M to 1M Urban 1M to 5M Urban.5M to 1M Urban <.5M Rural Less Developed Plot for emissions? -Data - Models - Predictions Least Developed

14 The growth era Phase transition in human activity around 18 Population (million), GDPppp ($billion) Global population and GDP Population GDPppp 5 Global 1 per capita 15 GDP 2? Changes over the past two millennia in global population, GDP (in Geary-Khamis international 199 dollars, a unit with constant purchasing power) and per capita GDP. Source: Historical Statistics for the World Economy: 1-23 AD, by Angus Maddison, Groningen Growth and Development Centre ( Per capita GDPppp ($/person) 1 1 doubling time = 45 y

15 Conclusions Brutal arithmetic Global "impact time scale" (T) = time to reach "safe" cumulative quota Q = 5 GtC for fossil fuels over 2-21 (probably too high) => T ~ 62 years at 24 FF emission T ~ 37 years at 2-26 growth rate (3.3%/y) The century of the finite planet By 25, the place we need to be (relative to present) is roughly Global Developed FF emission.4.25 Per capita emission.25.2 Perhaps 1/3 of this can be done with existing technologies, carbon tax etc Redefining growth to centre on quality of life and well-being Growth in material consumption: essential for well-being in 19 emerging threat to well-being in 2 disastrous for well-being in 21

16 Hilary Talbot

17 Recent climate and CO 2 Factors explaining observed warming from 185 to present: fluctuations in solar and volcanic forcing enhanced greenhouse gases From 197 onward, enhanced greenhouse forcing is dominant Fossil Fuel Emission (GtC/y) Atmoapheric [CO2] (ppmv) Temperature (deg C) Emissions [CO2] Temperature

18 Raupach et al. (28) in prep Development trajectories: energy Plot per capita primary energy against income, in 198, 1992, 24 Per capita Primary Energy (kw/person) E/P Per capita energy, E/P [kw/person] USA EU Japan D1 FSU China India D2 D3 World Australia Canada Taiw an KyotoA Income (g=gp/p) Per capita income (k$/y/person, Y2 US dollars)

19 August 26 Populations of largest urban regions and cities 1 largest urban regions 1: Tokyo (35.2M) 2: Mexico City (19.4M) 1: Casablanca (3.1M) Total: 693M (1.7% of global) 6 largest cities 1: Mumbai (12.78M) 9: Mexico City (8.5M) 6: Pune (3.6M) Total: 351M (5.4% of global) Population (M) Tokyo region (35M) Populations of largest urban regions and cities (25) Urban Regions Cities 2 We are talking about urban settlements of all sizes! Rank

20 Urban and rural incomes: China Per capita income of urban and rural households in China, Heilig, G.K. (1999) Can China feed itself? A system for evaluation of policy options. IIASA. ( Caption: This chart partly explains the attraction of cities and towns for China's rural population. Whereas average household income has risen significantly in rural areas, incomes in urban areas have increased even more. The gap between urban and rural income has remained almost unchanged. Source: China Statistical Yearbook, Beijing, 1998 (p.325) Note: Constant prices.

21 Conequences of urbanisation for CO 2 emissions: a statistical approach Basic identity: Let U = population of an individual urban unit (town or city) U min = population of smallest town U max = population of largest city F = P( G P) ( F G) = Pgh Consider ρ(u), the probability density function of U Definition: ρ(u) du is the fraction of the total population living in towns with populations between U and U + du We expect the affluence or percapita GDP (g = G/P) and the fossil-fuel intensity of GDP (h = F/G) to depend on the size of the urban unit: g = g(u), h = h(u) Then for a region with total population P, distributed among towns of size U with probability density function ρ(u), the total fossil fuel emission F is U U max min ( ) ( ) ( ) F = P g U h U ρ U du